library(dyngen)

This vignette demonstrates the different dynamic processes topologies (e.g. bifurcating and cyclic). If you haven’t done so already, first check out the installation instructions in the README.

You can find a full list of backbones using ?list_backbones. This vignette will showcase each of them individually.

Linear

set.seed(1)

backbone <- backbone_linear()

init <- initialise_model(
  backbone = backbone,
  num_cells = 500,
  num_tfs = 100,
  num_targets = 50,
  num_hks = 25,
  simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600)),
  verbose = FALSE
)
out <- generate_dataset(init, make_plots = TRUE)
out$plot

Bifurcating

set.seed(2)

backbone <- backbone_bifurcating()

init <- initialise_model(
  backbone = backbone,
  num_cells = 500,
  num_tfs = 100,
  num_targets = 50,
  num_hks = 25,
  simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600)),
  verbose = FALSE
)
out <- generate_dataset(init, make_plots = TRUE)
out$plot

Bifurcating converging

set.seed(3)

backbone <- backbone_bifurcating_converging()

init <- initialise_model(
  backbone = backbone,
  num_cells = 500,
  num_tfs = 100,
  num_targets = 50,
  num_hks = 25,
  simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600)),
  verbose = FALSE
)
out <- generate_dataset(init, make_plots = TRUE)
out$plot

Bifurcating cycle

set.seed(4)

backbone <- backbone_bifurcating_cycle()

init <- initialise_model(
  backbone = backbone,
  num_cells = 500,
  num_tfs = 100,
  num_targets = 50,
  num_hks = 25,
  simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600)),
  verbose = FALSE
)
out <- generate_dataset(init, make_plots = TRUE)
out$plot

Bifurcating loop

set.seed(5)

backbone <- backbone_bifurcating_loop()

init <- initialise_model(
  backbone = backbone,
  num_cells = 500,
  num_tfs = 100,
  num_targets = 50,
  num_hks = 25,
  simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600)),
  verbose = FALSE
)
out <- generate_dataset(init, make_plots = TRUE)
out$plot

Binary tree

set.seed(6)

backbone <- backbone_binary_tree(
  num_modifications = 2
)

init <- initialise_model(
  backbone = backbone,
  num_cells = 500,
  num_tfs = 100,
  num_targets = 50,
  num_hks = 25,
  simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600)),
  verbose = FALSE
)
out <- generate_dataset(init, make_plots = TRUE)
out$plot

Branching

set.seed(7)

backbone <- backbone_branching(
  num_modifications = 2,
  min_degree = 3,
  max_degree = 3
)

init <- initialise_model(
  backbone = backbone,
  num_cells = 500,
  num_tfs = 100,
  num_targets = 50,
  num_hks = 25,
  simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600)),
  verbose = FALSE
)
out <- generate_dataset(init, make_plots = TRUE)
#> Warning in .generate_cells_predict_state(model): Simulation does not contain all gold standard edges. This simulation likely suffers from bad kinetics; choose a different seed and rerun.
out$plot

Consecutive bifurcating

set.seed(8)

backbone <- backbone_consecutive_bifurcating()

init <- initialise_model(
  backbone = backbone,
  num_cells = 500,
  num_tfs = 100,
  num_targets = 50,
  num_hks = 25,
  simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600)),
  verbose = FALSE
)
out <- generate_dataset(init, make_plots = TRUE)
#> Warning in .generate_cells_predict_state(model): Simulation does not contain all gold standard edges. This simulation likely suffers from bad kinetics; choose a different seed and rerun.
out$plot

Trifurcating

set.seed(9)

backbone <- backbone_trifurcating()

init <- initialise_model(
  backbone = backbone,
  num_cells = 500,
  num_tfs = 100,
  num_targets = 50,
  num_hks = 25,
  verbose = FALSE
)
out <- generate_dataset(init, make_plots = TRUE)
out$plot

Converging

set.seed(10)

backbone <- backbone_converging()

init <- initialise_model(
  backbone = backbone,
  num_cells = 500,
  num_tfs = 100,
  num_targets = 50,
  num_hks = 25,
  simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600)),
  verbose = FALSE
)
out <- generate_dataset(init, make_plots = TRUE)
out$plot

Cycle

set.seed(11)

backbone <- backbone_cycle()

init <- initialise_model(
  backbone = backbone,
  num_cells = 500,
  num_tfs = 100,
  num_targets = 50,
  num_hks = 25,
  simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600)),
  verbose = FALSE
)
out <- generate_dataset(init, make_plots = TRUE)
out$plot

Disconnected

set.seed(12)

backbone <- backbone_disconnected()

init <- initialise_model(
  backbone = backbone,
  num_cells = 500,
  num_tfs = 100,
  num_targets = 50,
  num_hks = 25,
  simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600)),
  verbose = FALSE
)
out <- generate_dataset(init, make_plots = TRUE)
#> Warning in .generate_cells_predict_state(model): Simulation does not contain all gold standard edges. This simulation likely suffers from bad kinetics; choose a different seed and rerun.
out$plot

Linear simple

set.seed(13)

backbone <- backbone_linear_simple()

init <- initialise_model(
  backbone = backbone,
  num_cells = 500,
  num_tfs = 100,
  num_targets = 50,
  num_hks = 25,
  verbose = FALSE
)
out <- generate_dataset(init, make_plots = TRUE)
out$plot

Cycle simple

set.seed(14)

backbone <- backbone_cycle_simple()

init <- initialise_model(
  backbone = backbone,
  num_cells = 500,
  num_tfs = 100,
  num_targets = 50,
  num_hks = 25,
  verbose = FALSE
)
out <- generate_dataset(init, make_plots = TRUE)
out$plot

Showcase different backbones